There exists a need to be able to accurately detect potentially dangerous threat materials in a sample scene. Discrimination between a particular target material and a particular class of background material requires a few well-chosen wavebands. Different materials exhibit different spectral features. Therefore, a wide selection of wavelengths is necessary to address a wide variety of materials. As more types of targets and backgrounds are added to the sample scene, however, the number and location of wavelengths needed to discriminate between any given spectral pair grows rapidly.
To fully discriminate materials, spectral features must have differing intensities at different spectral locations. One possible solution would be to build many special-purpose sensors, each of which collects only a minimal set of wavebands needed for a limited set of targets and backgrounds. However, this approach has several drawbacks including cost, time, and limited results. Therefore, there exists a need for a system and method for multispectral imaging that enables cost-effective and robust results while discriminating target materials in a sample scene.